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M. Breiling:
"The Regional Model of Hermagor District: Endogenous Development and Exogenous Change Simulations";
Vortrag: Proceedings Advanced Techniques for Assessment of Natural Hazards, Innsbruck; 05.06.2000 - 07.06.2000; in: "Advanced Techniques for Assessment of Natural Hazards", (2000), S. 66 - 68.

Extended Abstract:
During 1988 and 1993 statistical models of Hermagor district - a mountainous area with 800 km² and
25,000 inhabitants, situated in Carinthia/Austria - were developed. The models should be integrated
to an overall model of Hermagor district (Breiling 1993) to anticipate the major challenges for the
regional development. Modelling is usually applied in a particular field of expertise, e.g. to assess the
probability or impacts of flooding or to forecast the timber harvest of the region, but not in
comprehensive planning and related decision making. The acceptable uncertainties of specialist
models can easily reach unacceptable levels in an integrated overall model of a region. Following
topics with associated couples were important for our considerations to the model development:
1. Interest: economy and environment
2. Region: inside and outside
3. Change: observed and forecasted
4. Integration: complete or partial
5. Scenarios: bad and good
1) Interest: economy and environment
The basic idea is to describe socio-economic and environmental development parameters in parallel.
For this purpose we develop three specialised models, one describes "economy" with help of an
economic-demographic model, one explains "environment" with help of a hydrological model and a
third one depicts the "economy-environment" interaction with help of a land use model. Our first idea
was to develop well functioning specialist models and then to integrate them in a quantitative way to
the overall model. We succeeded to simulate impacts in the case of the economic-demographic model
(Breiling, Charamza 1994), but could not come up with satisfying results in the case of the
hydrological and land-use model of the region, mainly because of reasons described under point 3).
2) Space: inside & outside
We differ between inside and outside of a region. The size of the region is determined from the
beginning. Certain factors influence from within, others govern from outside. Our specialised models
describe inside development and assume exogenous factors as stable in time. In the next stage we
consider even a change of the exogenous factors and use global climate change scenarios. This
change has an impact on all our specialised models. We can either quantify an impact in one or all
specialist models. Inside one can locally influence the situation while outside an influence is negligible.
The model shows also possibilities to counteract an expected event from inside the region, while the
cause of this event can rest outside.
3) Dynamics: observed & forecasted
In our case we use the period 1951 to 1991. All our data was recorded during this period. However,
the intervals of taking data varied. In the case of population or land use data, new entries came only
once in a decade, while hydrological and meteorological data was taken from daily records. Time
series of a decade could either contain only 2 or a maximum of 3653 data entries (in the case of
precipitation and run off data). In the case of hydrology there was only a 14 years period of
overlapping of precipitation and run-off data available, too short to serve our concept. Based on the
observation length of 40 years, forecasting may give reliable results for half of this period or 20 years.
Climate change scenarios with forecast horizons of 50 years and more have to be adjusted to our local
forecasting period.
4) Integration: complete or partial
While a specialist model forces the cause-effect relation in its own competence area, the integrated
model can show aggravating and trade off effects between several factors and evaluate the
specialised model. The more specialised models we employ, the richer the range of alternatives we
can choose from will be. Integration refers to different topics of interest and their spatial and dynamic
significance. All data had to be related to Hermagor district and the period 1951 to 1991. A complete
integration soon turned out to be an unachievable job. Nevertheless, we could see why the linking of
different specialised models did not work and what could be done to improve it. Finally, we were able
to partially integrate some of our topics and to interpret the outcome in a more precise way than
without the help of modelling.
5) Scenarios
The aim of the overall model is to demonstrate under what condition a certain kind of a "good" or
"bad" development can happen. One can test scenarios of scientists (for example global climate
change research) or decision makers (wishes of local politicians) and combine their expectations in a
future reference point. Can regional economic growth continue even under conditions of warming?
Does the construction of more lifts for skiers pay off if there will be a major warming in the next
decades? Can the number of catastrophes increase as a consequence of extreme weather events and
decreased resilience of the local environment? Is additional safety provision necessary? Will there be
enough money to finance safety provisions?
One can examine, if and under what conditions an endpoint can be reached. For example, we can
simulate a reduction of the population with x% or a destabilisation of y% of land in z years and
describe ways how such a situation could happen. The outcome differed widely according to our
assumptions of certain parameters. Rather small events could aggravate to large costs. Even under
conditions of a doubling of CO2 in the atmosphere landscape destabilisation could be balanced by
improved forest or water management.
In conclusion, integrated modelling can play a more important role. Planning can become increasingly
more powerful in giving appropriate information concerning the many alternatives to a possible future.
It helps to better manage complexity and to reduce surprise. While we cannot cover all topics that are
relevant for the regional development in an integrated model we will cover increasingly more topics
once we have started to construct it. We can set larger or smaller regional borders or concentrate on
a longer or shorter future. Our view on space and time scales will then become an equally important
subject for integration. The cause-effect relation is not limited to the same regional scale, but covers
everything from local to global. While threats may arise from the global scale, we should see
opportunities in the local one.

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